Detection of hypokinetic and hyperkinetic states

ABSTRACT

The present invention relates to an automated method of determining a kinetic state of a person. The method obtains accelerometer data from an accelerometer worn on an extremity of the person and processes the accelerometer data to determine a measure for the kinetic state. The present invention further relates to a device for determining a kinetic state of a person. The device comprises a processor configured to process data obtained from an accelerometer worn on an extremity of the person and to determine from the data a measure for the kinetic state. In the method and system the kinetic state is at least one of bradykinesia, dyskinesia, and hyperkinesia.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/997,540 filed on Dec. 10, 2010, which is a national stage under 35U.S.C. 371 of PCT/AU09/00751 filed on May 12, 2009, which claims thebenefit of priority from both Australian Provisional Patent ApplicationNo. 2008902982 filed on Jun. 12, 2008 and Australian Provisional PatentApplication No. 2009902616 filed on Jun. 9, 2009, the contents of all ofthe aforementioned are herein incorporated by reference in theirentireties.

TECHNICAL FIELD

This invention relates to analysis of kinetic state of a person bymonitoring motion symptoms to detect bradykinesia and/or dyskinesia orhyperkinesia.

BACKGROUND OF THE INVENTION

A range of diseases, medications, trauma and other factors can lead to aperson having motion symptoms such as dyskinesia, in which the person isin a hyperkinetic state, or bradykinesia, in which the person is in ahypokinetic state.

For example, bradykinesia is a key manifestation of Parkinson's disease.L-Dopa, or Levodopa, is often administered to patients havingParkinson's disease, and can have the effect of causing the patient tobecome dyskinetic for a period of time after administration. AsParkinson's disease progresses, the half life of L-Dopa shortens and theeffective dose range decreases, making dosage control extremelydifficult and complex. This is commonly managed by increasing the dosefrequency, sometimes by as much as ten doses each day in an attempt tocontrol symptoms and enable the patient to have a reasonable quality oflife. Thus, patients with Parkinson's disease may experience periods ofbradykinesia, dyskinesia and normal motor function several times a dayand throughout the course of a single dose of L-Dopa.

Even if a satisfactory dosage regime is reached at one point in time,the progressive nature of Parkinson's disease means that neurologistsmust regularly review a patient's symptoms in order to effectivelycontrol the patient's ongoing treatment dosage. Without objective andongoing monitoring it is very difficult for physicians to avoidprescribing either an excessive dose which overly increases episodes ofdyskinesia, or an inadequate dose which does not prevent episodes ofbradykinesia. Furthermore there is no objective measure to say whether achange in dose was effective in improving symptoms.

From clinical observation, skilled neurologists can usually detect theexistence of bradykinesia and dyskinesia. In one approach, the observingphysician gives a score in the range of 0 to 20 to indicate the severityof the observed episode. FIG. 1 shows scores given by threeneurologists, with each plotted point representing the scores given bytwo neurologists when observing a single dyskinetic episode. Scores forNeurologist 1 (triangles) and Neurologist 3 (circles) are plottedagainst scores from Neurologist 2. As is evident, the subjective natureof this scoring approach leads to considerable variation. In one extremeexample, Neurologist 2 scored one dyskinetic episode as being ofseverity 10 (being quite severe when noting that the highest score evergiven by Neurologist 2 was a 13), whereas Neurologist 3 scored the sameepisode as being of severity 0 (no dyskinesia observed). Thus, whilephysicians can usually detect dyskinesia and other kinetic states duringobservation, these states are not easily quantified, making dosagecontrol very subjective.

Further, clinical observation typically only occurs over a short periodof patient attendance, usually of the order of tens of minutes, onceevery 6 or 8 weeks. Fluctuations in kinetic state throughout the day andfrom one day to the next significantly complicate attempts at assessingthe patient's kinetic state. Clinicians often rely on the patient'srecollection and/or written diaries to gain an understanding of theongoing kinetic state of the patient between clinical appointments.However patients can rarely give objective scores, and the effect of akinetic episode itself can often make it difficult for a patient to makeany record whatsoever of the nature of and timing of motor fluctuations.

Another common symptom, of Parkinson's Disease for example, is tremor.Parkinsonian tremor is slower than most forms of tremor with a frequencyof 4-6 cycles per second. Compared with other elements of movement,tremor consists of oscillations of relatively few frequency components.On spectral analysis, it appears as a discrete peak in a narrowfrequency range (4-6 Hz), usually clearly above the frequency range ofnormal movement (less than 4 Hz). Tremor has been the subject ofnumerous studies and is particularly amenable to study with spectralanalysis. Tremor is relatively easy to detect because it is a continuousrepetitive movement, giving a sinusoidal signature, which is simple todistinguish from normal human motions which are rarely so continuous.Tremor is far less a problem in management of Parkinson's Disease thandyskinesia and bradykinesia. Attempts have been made to infer a person'sbradykinetic state from measurements of tremor, in an attempt toregulate medication. However for many patients there is not a closecorrelation between tremor and bradykinesia, making it likely thatmedication will be inaccurately administered using this technique.

Any discussion of documents, acts, materials, devices, articles or thelike which has been included in the present specification is solely forthe purpose of providing a context for the present invention. It is notto be taken as an admission that any or all of these matters form partof the prior art base or were common general knowledge in the fieldrelevant to the present invention as it existed before the priority dateof each claim of this application.

Throughout this specification the word “comprise”, or variations such as“comprises” or “comprising”, will be understood to imply the inclusionof a stated element, integer or step, or group of elements, integers orsteps, but not the exclusion of any other element, integer or step, orgroup of elements, integers or steps.

SUMMARY OF THE INVENTION

According to a first aspect, the present invention provides an automatedmethod of determining a kinetic state of a person, the methodcomprising:

obtaining data from an accelerometer worn on an extremity of the person;and

processing the data to determine a measure for the kinetic state, thekinetic state being at least one of bradykinesia, dyskinesia, andhyperkinesia.

According to a second aspect, the present invention provides a devicefor determining a kinetic state of a person, the device comprising:

a processor configured to process data obtained from an accelerometerworn on an extremity of the person and to determine from the data ameasure for the kinetic state, the kinetic state being at least one ofbradykinesia, dyskinesia, and hyperkinesia.

According to a third aspect, the present invention provides a computerprogram product comprising computer program means to make a computerexecute a procedure for determining a kinetic state of a person, thecomputer program product comprising:

computer program code means for obtaining data from an accelerometerworn on an extremity of the person; and

computer program code means for processing the data to determine ameasure for the kinetic state, the kinetic state being at least one ofbradykinesia, dyskinesia, and hyperkinesia.

Notably, the present invention thus provides for a determination to bemade as to a kinetic state of a person based on measurements obtainedfrom a single accelerometer worn on an extremity of the person. In thisspecification the term kinetic state is defined to be a movementdisorder state. This invention recognises that a single sensor worn onan extremity provides adequate movement-related data to enable adetermination of a state of bradykinesia and/or dyskinesia orhyperkinesia to be made. Embodiments of the invention may thus beparticularly suitable for frail, elderly or disabled persons for whomfitting more than a single sensor becomes impractical. In someembodiments the accelerometer is worn below the elbow, such as on thewrist. In other embodiments the sensor may be worn below the knee, suchas on the ankle.

Further, the present invention provides for an automated determinationof a kinetic state which is at least one of bradykinesia and dyskinesia,thus providing a technique which does not rely on a potentiallyinaccurate inference of bradykinesia based on a measure of tremor.

In preferred embodiments, the accelerometer data is processed in orderto determine both a measure for bradykinesia and a measure fordyskinesia.

Bradykinesia

In some embodiments in which a measure of bradykinesia is determined,digital data from the accelerometer is band pass filtered to extractdata for a band of interest. The band of interest may have a lower endcut off frequency which is selected to remove DC. The lower end cut offfrequency for example may be in the range of 0.05 Hz to 1 Hz, preferablybeing 0.2 Hz. The band of interest may have an upper end cut offfrequency which is selected to eliminate high frequency components whichin general do not arise from normal human motions. The upper end cut offfrequency for example may be in the range of 3 Hz to IS Hz, preferablybeing 4 Hz. An upper cut off of around 4 Hz may be beneficial inavoiding or minimising of the influence of tremor, which is usually over4 Hz.

Additionally or alternatively, in some embodiments in which a measure ofbradykinesia is determined, a time block or “bin” of digitalacceleration data is extracted from the time series of data andconsidered in isolation, with each bin being of a time duration which isselected to be small enough that relatively regular measures ofbradykinesia are determined, while being long enough to provide areasonable likelihood of a significant movement by the person duringthat bin. For example, the bin duration may be in the range of twoseconds to 60 minutes, more preferably being in the range of 15 secondsto four minutes, and most preferably being in the range of 30 seconds totwo minutes.

Additionally or alternatively, in some embodiments in which a measure ofbradykinesia is determined, the digital data is searched for a maxima,preferably using a moving mean having a window length which is afraction of the duration of a normal human motion, for example thewindow length of the moving mean may be in the range of 0.02 seconds to30 seconds, and may be substantially 0.2 seconds. The window in whichthe data is found to have the highest mean is taken to represent themovement of peak acceleration by the person. Such embodiments recognisethat a person in a normal kinetic state generally has movements ofhigher peak acceleration than a bradykinetic person, and that the peakacceleration is thus an indicator by which a bradykinetic state may bedetected and quantified. In embodiments assessing data bins, for bin ithe highest mean is referred to as PKi, being the window of peakacceleration. A threshold may be applied whereby values of PKi below thethreshold are excluded to allow for the possibility that a bradykineticperson and a normally kinetic person may each simply remain still forsome bins.

Additionally or alternatively, in some embodiments in which a measure ofbradykinesia is determined, a sub-bin comprising a plurality of datapoints both before and after a peak acceleration are obtained. Thesub-bin preferably comprises a number of data points which is a power oftwo, and the sub-bin is preferably symmetrically positioned about thepeak acceleration. The sub-bin preferably comprises data points obtainedover a period of time which is substantially the same as the duration ofa normal single human motion, for example the duration of the sub-binmay be in the range of 0.5 seconds to 30 seconds, more preferably in therange of 1 second to 3 seconds, and for example may be substantially2.56 seconds. The sub-bin is further preferably a small fraction of thelength of an associated bin, if any. A spectral analysis of the sub-binis preferably conducted, for example by performing a Fast FourierTransform on the data of the sub-bin to obtain sub-band spectralmeasures. The sub-bands may be of a width which is around one fourth ofa band of interest. The sub-bands may be of a width in the range of 0.1Hz to 2 Hz, more preferably in the range of 0.6 Hz to 1 Hz, and may besubstantially 0.8 Hz. The sub-bands may be overlapping in the frequencydomain, for example eight partially overlapping sub-bands may beconsidered.

Such embodiments thus provide for spectral components of the singlemovement of peak acceleration to be obtained, recognising that if theperson's peak movement has strong low frequency components this isindicative of bradykinesia. Some embodiments may thus identify whichsingle sub-band has greatest power and give a stronger indication of thepresence of bradykinesia when a low frequency sub-band has greatestpower. Additionally or alternatively a weighting may be applied to someor all of the sub-band spectral measures to produce a weighted meanspectral power MSP_(i) such that a greater indication of bradykinesia isgiven when the maximum (MSP_(i)) is small and exists in lower frequencysub-bands, and a lesser indication of bradykinesia is given when themaximum (MSP_(i)) is high and exists in higher frequency sub-bands.

Additionally or alternatively, in some embodiments in which a measure ofbradykinesia is determined, a plurality n of consecutive bins may beconsidered, a PKi and MSPi determined for each bin, and from across then bins selecting the largest value of PKi (PK_(i,max)) and selecting thelargest value of MSPi (MSP_(i,max)). A bradykinesia score BK may then becomputed as:BK=PK _(i,max)×MSP_(i,max)Alternatively, a bradykinesia score may be computed as:BK=A×log_(c)(PK _(i,max)×MSP_(i,max))−Bwhere A, c and B are selectable tuning constants. In a non limitingexample A=16.667, n=10 and B=116.667. Such embodiments recognise that,if a person is remaining still, individual bins may carry littleinformation to enable differentiation between a normally kinetic personand a bradykinetic person. Consideration of a sequence of bins increasesthe likelihood that actual movements are being considered.

Additional or alternative embodiments may provide for the BK score to beinfluenced by whether the person goes for long periods without movement.Such embodiments recognise a key differentiating factor between normallykinetic persons and bradykinetic persons, which is that normally kineticpersons rarely if ever remain completely motionless for any significantperiod of time, whereas bradykinetic persons can remain motionless forsignificant periods. Such embodiments might for example consider athreshold acceleration value of the PK_(i) of multiple bins, such as themode of the PK_(i) values, which will take a small value. Should thePK_(i) of the person go for a long period (referred to as a quiet timeor QT) without exceeding the threshold, this may in such embodiments betaken to indicate a bradykinetic state. For example, the bradykinesiascore might be computed as:BK=A×log_(c)(PK _(i,max)×MSP_(i,max))/QT ^(m) −Bsuch that a large QT reduces the BK score, thereby more stronglyindicating bradykinesia. The value of m is preferably greater than orequal to 1, such that long periods of QT more strongly influence the BKscore.

It is noted that such embodiments produce a BK score which has a largervalue for normally kinetic persons and a smaller value closer to zerofor bradykinetic persons, consistent with common clinical subjectivemeasures.

In another embodiment, QT may be used as an additional indicator of BKin its own right. A large QT would be very BK.

A moving mean of multiple consecutive BK scores may be output to smooththe results. In some embodiments the measure of bradykinesia may bedetermined repeatedly over time, for example the measure may bedetermined every few minutes. In such embodiments, a cumulativebradykinesia score comprising a sum of the individual measures may bedetermined in order to provide a cumulative indication of the kineticstate. For example the cumulative score may be determined over thecourse of a single dose of L-dopa, or over the course of a day.

Some embodiments of the invention thus recognise that bradykineticmovements have lower acceleration and velocity, and that the lowfrequency, amplitude, velocity and acceleration of bradykineticmovements is manifested in a spectral analysis by a relativepreponderance of low frequencies and reduced power in all frequencies.

Dyskinesia

In some embodiments in which a measure of dyskinesia is determined, thedigital data from the accelerometer is band pass filtered to extractdata for a band of interest. The band of interest may have a lower endcut off frequency, which is selected to remove DC components, forexample being in the range of 0.05 Hz to 2 Hz, preferably being 1 Hz.The band of interest may have an upper end cut off frequency, which isselected to eliminate higher frequency components which in general donot arise from normal human motions, for example being in the range of 3Hz to 15 Hz, preferably being 4 Hz. An upper cut off of around 4 Hz maybe beneficial in avoiding or minimising the influence of tremor which isusually over 4 Hz.

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a time block or “bin” of digital accelerationdata is extracted from the time series of data and considered inisolation, with each bin being of a time duration which is selected tobe small enough that relatively regular measures of dyskinesia aredetermined, while being long enough to provide a reasonable likelihoodthat a normally kinetic person will have periods of little or nomovement during that bin. For example the bin duration may be in therange of ten seconds to 10 minutes, more preferably being in the rangeof 30 seconds to four minutes, and most preferably being substantiallytwo minutes. Such embodiments recognise that a differentiating factorbetween a normally kinetic person and a dyskinetic person is that anormally kinetic person will have periods of little or no movement,whereas a dyskinetic person generally can not keep still and thus hasfew periods of little or no movement.

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, the data may be compared to a threshold valueand a period or proportion of time for which the data remains below thethreshold may be determined. Such a measure relates to the period orproportion of time for which the person has reduced movement, and isreferred to herein as the time of reduced movement (T_(RM)). Thethreshold value may be the mean value of the data. A moving mean of thedata may be what is compared to the threshold, to reduce the effects ofnoise. For example a window length of the moving mean may be in therange of 0.5 seconds to 4 seconds, preferably substantially one second.A T_(RM) measure produced in such embodiments will be small fordyskinetic persons as they have few periods of no movement, but will belarger for normally kinetic persons, thereby enabling dyskinesia to bedetected and quantified.

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, the data may be compared to a threshold valueand a power measure of data which falls below the threshold may bedetermined. Such embodiments recognise that for a dyskinetic person databelow the threshold will have a greater power than for a normallykinetic person, as a dyskinetic person will rarely be truly motionless.The threshold may be the mean value of the data, which will take ahigher value for dyskinetic persons and lead to a higher power of databelow the threshold, thereby enhancing the ability to detect andquantify dyskinesia. The power measure of the data which falls below themeasure may comprise the mean spectral power (SP_(RM)) obtained byperforming a Fast Fourier Transform on the data below the threshold. Theroot-mean-square (RMS) value of the SP_(RM) may be taken to obtainSP_(RM.RMS).

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a variance (VAR) of the frequency componentsof the data may be obtained. Such embodiments recognise that dyskinesiaoften yields movements at a wide range of frequencies leading to a largeVAR, whereas a normally kinetic person tends to move at a similar speedfor most motions leading to a small VAR. The VAR thus provides a furthermeasure by which dyskinesia may be detected and quantified.

In some embodiments in which a measure of dyskinesia is determined, adyskinesia score might be computed as:DK=A×log_(c)(SP_(RM) /T _(RM))

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a dyskinesia score might be computed as:DK=A×log_(c)(Acc×SP_(RM) /T _(RM))

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a dyskinesia score might be computed as:DK=A×log_(c)(RMS_(RM) /T _(RM))

where A, and c are selectable tuning constants, T_(RM) is the time ofreduced movement and RMS_(RM) is the root mean-square value of theaccelerometer data below the threshold value.

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a dyskinesia score might be computed as:DK=A×log_(c)(VAR/T _(RM))

Additionally or alternatively, in some embodiments in which a measure ofdyskinesia is determined, a dyskinesia score might be computed as:DK×A×log_(c)(VAR×SP_(RM) /T _(RM))

As SP_(RM), SP_(RM.RMS), VAR and Acc are large for dyskinetic persons,and T_(RM) is small for dyskinetic persons, the above scores indicatedyskinesia with a high number, consistent with common clinicalsubjective measures.

A moving mean of multiple consecutive DK scores may be output to smooththe results. In some embodiments the measure of dyskinesia may bedetermined repeatedly over time, for example the measure may bedetermined every few minutes. In such embodiments, a cumulativedyskinesia score comprising a sum of the individual measures may bedetermined in order to provide a cumulative indication of the kineticstate. For example the cumulative score may be determined over thecourse of a single dose of L-dopa, or over the course of a day.

Some embodiments of the invention thus recognise that dyskineticmovements have greater power, increased amplitude and are of acontinuous relentless quality.

In some embodiments, the data is processed to produce both a measure ofbradykinesia and a measure of dyskinesia. Such embodiments recognisethat a person may suffer both bradykinesia and dyskinesia simultaneouslyor in close succession and that each state can be independentlyquantified from the data returned by the accelerometer.

Thus, some embodiments of the present invention provide for objectivelydetecting and quantifying bradykinetic and/or dyskinetic states, whichis of importance in assessing the effect of therapeutic agents, both inclinical trials and in the normal clinical setting, especially to guideuse of disease modifying interventions. These embodiments achieve this,even where kinetic symptoms fluctuate, by taking measurementssubstantially continuously or frequently throughout the day. Moreover,rather then relying on subjective measures of the patient orneurologist, embodiments of the invention provide for an objectivemeasure so that an automated comparative analysis can be undertaken overa longer period, such as over a 24 hour period. Such embodimentsrecognise that a longer period of analysis is beneficial in order tobetter assess the effect of therapeutic agents such as L-Dopa.

In some embodiments the accelerometer is a 3-axis accelerometer giving,for each axis of sensitivity, an output proportional to accelerationalong that axis. Each output is preferably sampled to obtain datarepresenting acceleration over time. For example 100 Hz sampling may beused.

In some embodiments of the second aspect of the invention, the devicemay be a central computing device which is remote from the person andconfigured to receive data from the accelerometer via a communicationsnetwork. In such embodiments, the central computing device can befurther configured to communicate the determined measure of the kineticstate to a physician or clinician or the like associated with theperson.

In other embodiments of the second aspect of the invention, the devicemay be a body-worn device comprising an accelerometer from which thedata is obtained. Such embodiments may further comprise an output means,such as a display, to indicate the determined measure of the kineticstate to the person. In such embodiments the processor of the device mayfurther be configured to use the measure of the kinetic state to updatea medication regime of the person and to indicate the updated regime tothe person. The medication regime may be updated by altering a dose ofmedication and/or updating a timing of a dose of medication.

BRIEF DESCRIPTION OF THE DRAWINGS

An example of the invention will now be described with reference to theaccompanying drawings, in which:

FIG. 1 is a plot of dyskinesia scores given by three neurologists, witheach plotted point representing the scores given by two neurologistswhen observing a single dyskinetic episode;

FIG. 2 is a diagrammatic view of a means for detection of variousParkinsonian clinical states in accordance with an embodiment of theinvention;

FIG. 3 illustrates kinetic state monitoring and reporting in accordancewith one embodiment of the invention;

FIG. 4 is a plot of dyskinesia scores, with each point showing a scoregenerated by one embodiment of the invention for a single dyskineticepisode plotted against the average of scores given by threeneurologists observing the same episode;

FIG. 5 illustrates the Average Peak Acceleration (APA) achieved duringtask 2 (bradykinesia score) plotted for each subject group (C=Controls,B=bradykinetic and D=dyskinetic subjects);

FIG. 6A illustrates the power spectrum obtained from a normal subjectwhile sitting still (dotted line, task 3) and while performing voluntarymovements (task 1, heavy line);

FIG. 6B illustrates the spectral output when the subject was asked touse the fore finger to track 2 Hz and 4 Hz oscillations moving acrossthe face of an oscilloscope;

FIG. 7 illustrates the power spectrum obtained from a normal subjectwhile writing the word “minimum”;

FIG. 8 is a plot of the MSP for normal (C), bradykinetic (B) anddyskinetic (D) subjects for each spectral band and performing Task 1 orTask 3;

FIG. 9A is a plot of APA against the ABS;

FIG. 9B illustrates changes in bradykinesia of a single patient plottedagainst time after a dose of L-dopa, with the heavy line beingbradykinesia as determined by the APA and the dotted line beingbradykinesia as determined by the ABS;

FIG. 10A illustrates the IMS plotted against the ADS;

FIG. 10B illustrates changes in dyskinesia of a single patient plottedagainst time after a dose of L-dopa, with the heavy line beingdyskinesia as determined by the APA and the dotted line being dyskinesiaas determined by the ADS;

FIG. 11 is a resulting scan of the patient and resultant determinationusing the apparatus and system of the invention;

FIG. 12 illustrates a general-purpose computing device that may be usedin an exemplary system for implementing the invention;

FIG. 13 shows IMS scores for a wrist compared to IMS scores for thewhole body;

FIG. 14 is a graph illustrating substantially continuous DK and BKscoring for an individual throughout the course of a day;

FIG. 15 is a graph illustrating an alternative manner in which theresults of the invention may be presented, by plotting a cumulative sumof DK scores for the period following each dose together with the valueof BK scores throughout the day; and

FIG. 16 is a graph which plots DK and BK scores for a patient who isdyskinetic.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 2 is a diagrammatic view of a device 15 for detection of variousParkinsonian or kinetic states in accordance with an embodiment of theinvention. The device 15 is wrist mounted which the present inventorshave recognised provides a sufficiently accurate representation of thekinetic state of the whole body. For example, IMS scores for a wristcompared to IMS scores for the whole body are shown in FIG. 13 ,illustrating that the wrist gives adequate kinetic state information.The device 15 comprises three elements for obtaining movement data of alimb of a person. The device 15 comprises a motion monitor 21 in theform of an accelerometer, an assessor 22 for recording and analysis ofthe received data in a manner that provides an objective determinationof bradykinesia and dyskinesia, and an output means 23 for outputtingobjective determination of bradykinesia or dyskinesia over time periodsso as to allow a clinician to prescribe medications or to allow theperson to better understand their own kinetic state.

The device 15 is a light weight device which is intended to be worn onthe most affected wrist of the person. The device is mounted on anelastic wrist band so as to be firmly supported enough that it does notwobble on the arm and therefore does not exaggerate accelerations. Thedevice is configured to rise away from the person's wrist by a minimalamount so as to minimise exaggeration of movements. The device may be ona wrist band secured by a buckle, whereby the act of unbuckling andremoving the device breaks a circuit and informs the logger that thedevice is not being worn. The patient preferably wears the device for atleast 30 minutes prior to taking their first medication for the day,until bedtime. This allows the device to record early morningbradykinesia, which is usually at its worst at this time. The devicethen goes on to record kinetic responses to all medications for the day.

The accelerometer 21 records acceleration in three axes X, Y, Z over thebandwidth 0-10 Hz, and stores the three channels of data in memoryon-board the device. This device has 250 MB of storage so as to allowdata to be stored on the device for up to 3 days, after which the devicecan be provided to an administrator for the data to be downloaded andanalysed. Additionally, in this embodiment, when the device is removedeach night for patient sleep time, the device is configured to be placedin and interface with a dock so as to have the device transfer the datato the dock which then transmits the data via wireless broadband toanalysis servers at the main company (see 114 in FIG. 3 ). The interfacewith the dock also provides for batteries of the device to be recharged.

As a wrist-worn device intended for potentially frail persons, thedevice is of minimal size and weight. Further, for this reason thedocking interface is designed such that the device simply falls intoplace to effect connections of the interface, and provides a very clearfeedback that the connection has been made. In one alternativeinformation from the data logger may be transmitted wirelessly byBluetooth or the like to a PDA (Personal Digital Assistant), kept withthe patient to avoid the need for docking to effect data transfer.

FIG. 3 illustrates kinetic state monitoring and reporting in accordancewith one embodiment of the invention. A patient 112 is wearing thedevice of FIG. 2 . The device 15 logs accelerometer data andcommunicates it to a central computing facility 114. The computingfacility 114 analyses the data using an algorithm (discussed furtherbelow), to obtain a time series of scores for the bradykinetic state ofthe person 112 and a time series of scores for the dyskinetic state ofthe person. These scores are reported to a neurologist 116 in a formatwhich can be rapidly interpreted by the neurologist to ensure efficientuse of the neurologist's time. The report shows major movementcategories and is emailed directly to the physician or made available ona website. From this report the patient's medication protocol can beoptimised. The neurologist 116 then interprets the kinetic state reportand updates a medication prescription of the patient accordingly.

The accelerometer measures acceleration using a uniaxial accelerometerwith a measurement range of +/−4 g over a frequency range of 0 to 10 Hz.Alternatively a triaxial accelerometer can be used to provide greatersensitivity.

The device stores data for up to 16 hours per day, for up to 7 days. Thestored data is then transferred to the central computing facility 114manually or by wireless broadband, or via Bluetooth radio to a PDA, orthe like. The recording system is thus fully mobile and can be worn athome by the patient.

In this embodiment algorithms are applied to the obtained data by acentral computing facility 114 in order to generate a dyskinesia scoreand a bradykinesia score.

Bradykinesia Scoring Algorithm

The algorithm for producing an automated bradykinesia score (BK) stemsfrom the recognition that bradykinetic subjects have longer intervalsbetween movement and when they do move it is with lower acceleration.Bradykinetic patients thus have a low percentage of time with movement.Normally kinetic persons have a higher percentage of time in which theyare moving and a higher peak acceleration of movements. In keeping withpresently used subjective measures based on clinical observation, inthis algorithm a low BK score indicates more severe bradykinesia, whilea high BK score indicates little or no bradykinesia. The bradykinesiascoring algorithm operates on the recorded data in the following steps.

BK1: the data is band-pass filtered to extract components in the range0.2 to 4 Hz, in order to remove DC, wrist rotation, tremor above 4 Hz,and accidental bumping of the logger and the like.

BK2: Retrieve a short bin of data at a time, being 30 seconds or 3000data points per bin in this embodiment. The bin length is long enough toprovide a good chance that the person will undertake significantmovement within that bin period such that parameters PK_(i) andSP_(maxi) (described further below) are likely to arise from such amovement.Steps BK3 to BK9 are designed to find a maximum acceleration in the binand the frequency at which this acceleration occurred. This recognisesthat normal movements have higher accelerations which occur at higherfrequencies, while bradykinesia is characterised by lower peakaccelerations occurring at lower frequency.BK3: the i^(th) bin is searched for a maximum acceleration value using a0.2 second (20 data points) moving mean to eliminate noise. The 0.2second period with the highest mean is deemed to be the peakacceleration, PKi. Noise may in other embodiments be eliminated bytaking a median, or by selecting high values out, or by low passfiltering.BK4: X points either side of PKi are collected, to create a sub-bin of2× data points to be used for a FFT. In this embodiment 128 points aretaken either side to produce a sub bin of 256 points (2.56 s).BK5: A FFT is performed on the peak acceleration sub-bin, on the rawaccelerometer signal, to find the frequency components present aroundthe PKi.BK6: Overlapping 0.8 Hz bands are considered, namely:

-   -   A 0.2-1.0 Hz    -   B 0.6-1.4 Hz    -   C 1.0-1.8 Hz    -   D 1.4-2.2 Hz    -   E 1.8-2.6 Hz    -   F 2.2-3.0 Hz    -   G 2.6-3.4 Hz    -   H 3.0-3.8 Hz        The band which contains the maximum mean spectral power SPmaxi        is identified.        BK7: The value in each of the eight frequency bins is weighted        as follows:    -   A×0.8    -   B×0.9    -   C×1.0    -   D×1.1    -   E×1.2    -   F×1.3    -   G×1.4    -   H×1.5        A maximum weighted mean spectral power (MSP_(MAX)) is identified        from the weighted band values, using a linear look-up function.        BK8: A high MSPmax with high frequencies and high amplitudes is        taken to be more likely to indicate a non-bradykinetic state,        while a small MSPmax is more likely to indicate bradykinesia.        BK9: Steps BK3 to BK8 are repeated for each 30 second bin to        obtain a series of MSPmax.i values.        BK10: The biggest movements over a group of the analysis bins        are identified and recorded. The group of analysis bins may        extend over four bins to yield a BK score every 2 minutes, or        may extend over six bins to yield a BK score every 3 minutes,        for example. The maximum PKi of the group of bins and the        largest weighted MSPmax.i of the group of bins are selected, and        it is noted that these two values might not arise from the same        bin. A Bradykinesia Score is produced by calculating:        BK=A×log 10(MSPmax×PKi)−B        This step thus operates upon the “best” or strongest movements        in each 2-3 minute window. The BK score is then plotted against        time.        BK11: A moving mean is taken of BK values over a 2 to 10 minute        window (window length being a variable) and plotted against        time, so as to filter the result for intuitive presentation to a        neurologist.

The BK score produced by this algorithm thus enables a change in BK overtime from each medication to be assessed, and the relative change in BKfrom the time of medication to be measured. This also allows anassessment of the percentage of time for which the patient is at each BKscore for each day or each medication period. Noting that normallykinetic people can behave in a bradykinetic manner for short periods oftime it is important to assess both the persistency and depth of theperson's bradykinesia, which is made possible by this embodiment.

Dyskinesia Scoring Algorithm

The algorithm for producing an automated dyskinesia score stems from therecognition that dyskinetic subjects have few intervals or pausesbetween movement, while non-dyskinetic people will have longer periodsof no movement. Dyskinetic persons will also move with a greaterspectral power. This algorithm thus works to distinguish betweennormally kinetic people undergoing periods of excess voluntary movementand dyskinetic persons undergoing excess involuntary movement. Thedyskinesia scoring algorithm operates on the recorded data in thefollowing steps.

DK1: Band-pass filter the raw data to extract components in the range1-4 Hz, in order to remove DC, wrist rotation, tremor and bumping of thesensor.

DK2: null

Steps DK3 to DK7 aim to remove sections of data that are above the meanacceleration, in an attempt to remove voluntary normal movements fromthe data set.

DK3: The data is broken down into 120 s bins which are each consideredin isolation. The bin width is a variable, in this embodiment comprising12000 data points. Longer bin periods are more likely to excludemovements of high acceleration because the majority of the signal willhave smaller amplitude.DK4: For each 120s bin i the mean acceleration amplitude (Acc_(i)) ismeasured, using the absolute amplitude of the data. Acc_(i) is used as athreshold below which data is deemed to represent “reduced movement”.DK5: A one second (100 data point) moving point mean is calculatedacross the bin.DK6: Any one second duration of data for which the mean acceleration islarger than Acc_(i) is removed from further consideration, in an attemptto exclude voluntary normal movements.DK7: The remaining data in the bin is assumed to relate to periods ofreduced movement and therefore is referred to as the reduced movement(RM) data set. The time period of the reduced movement within the bin isT_(RM). The remaining RM data in the bin is simply concatenated.Steps DK8-DK12 aim to measure the properties of the “non-voluntary”movement set remaining in the data, assessing several ways of measuringthe power in the non-voluntary movements of the RM data. It is notedthat dyskinetic patients have high power in their non-voluntarymovements.DK8A: a FFT is performed on the RM data set in each 120s bin. The meanspectral power for the RM in each 120s bin is the SP_(RM). This is forthe 1-4 Hz range due to the filtering at DK1. In dyskinesia this powerwill be higher than for normally kinetic persons.DK8B: The RMS value of the Reduced Movement data set absolute values istaken, to give the reduced movement power.DK8C: The variance (VAR) or standard deviation of the frequencies ineither the full 120s bin or in the RM data set is obtained.DK9: A DK score is calculated as:DKsp=ASP _(RM) /T _(RM)and DKsp is plotted.DK10: A DK score is calculated as:DKacc=log_(c)(Acc_(i)×SP_(RM))/T _(RM)and DKacc is plotted.DK11: A DK score is calculated asDKrms=A log_(c) RMS_(RM) /T _(RM)and DKrms is plotted.DK12: A DK score is calculated asDKvar=A log_(c) VAR/T _(RM)and DKvar is plotted.

A moving mean is taken of DK values over a 2 to 10 minute window (windowlength being a variable) and plotted against time, so as to filter theresult for intuitive presentation to a neurologist. Further, apercentage of time for which a patient is at different absolute DKscores for each day or each medication period is assessed. Thisrecognises that a normally kinetic person can undergo dyskinetic-likemovements for short periods, but that only dyskinetic patients have arelentless nature to their movements, which is what is measured in thisapproach.

This embodiment further provides for DK scores from a daily medicationperiod, for example a 9:00 AM to 12:00 PM period, to be averaged overmultiple days to obtain a stronger measure.

FIG. 4 is a plot of dyskinesia scores, with each point showing a DKscore generated by one embodiment of the invention for a singledyskinetic episode plotted against the average scores of dyskinesiagiven by three neurologists observing the same event. As can be seen thepresent invention compares favourably with the average score of threeneurologists (referred to as the “Gold standard”), (specificity 93.6%;sensitivity 84.6%), demonstrating that this embodiment is an acceptablereplacement for daily clinical monitoring.

FIG. 11 illustrates results obtained using the system of FIG. 2 andusing the above algorithms, for one patient. The patient woke at 6:15 amand put the wrist recording device on. Her movements caused the deviceand algorithm to give a very low BK score of BK4 at this time, whichshows her to be very bradykinetic which is the principle feature ofParkinson's disease. She then took two tablets of L-Dopa at 7:00 am butremained bradykinetic until the tablets were absorbed and there wasenough concentration in the brain to start to reduce her bradykinesia.From about 8:00 am until 9:30 am her bradykinesia continued to improvefrom BK4 up to BK1, BK1 being normal pattern movement. However, theconcentration of L-Dopa at this stage also started to introduce peakdose dyskinesia at about 9:00 am. She relapsed into BK state near 10:00am. Her second medication was taken at 10:45 am which soon returned herto a normal BK score of BK1. Dyskinesia developed again around 12:30 pm.

As will be appreciated such a simultaneous, ongoing and objectivemeasure of both bradykinesia and dyskinesia provides a neurologist withdetailed information to assist in formulating a suitable regime ofmedication. For example, in response to this recording a neurologist mayelect to move the first dose of L-Dopa to earlier in the morning toreduce her bradykinesia time, then make the time interval to the seconddose somewhat shorter while maintaining the interval to the third dose.The aim for this patient would be to maintain BK for a higher percentageof time in the BK1 state, while also aiming to reduce the DK score sothat less time is spent in DK2 and DK3 states. Naturally, furthermeasurements can be taken in accordance with the present invention tomonitor the effect of such a change.

This embodiment thus provides for the bradykinetic and dyskinetic statesof the person to be recognised and quantified with high selectivity andsensitivity, even when the person is carrying out normal dailyactivities across a range of naturalistic movements and not controlledmovements in a clinical environment.

FIG. 14 illustrates substantially continuous DK and BK scoring for anindividual throughout the course of a day. L-dopa treatments were takenat the times indicated by the vertical lines. This figure produced bythe present embodiment of the invention clearly indicates that thepatient has very low dyskinesia and very significant bradykinesia,enabling a neurologist to quickly deduce that the patient appears to beundertreated.

FIG. 15 illustrates an alternative manner in which the results of thepresent technique may be presented, by plotting a cumulative sum of DKscores for the period following each dose. The actual DK scores areshown in faint lines with the cumulative DK score (CUSUM DK) indicatedby the solid line. Again, the time of each medication is indicated by avertical line. A flat CUSUM indicates a normal kinetic state, and soFIG. 15 illustrates that this patient experiences significantdyskinesia, particularly during the afternoon. In this case the presentinvention thus provides the neurologist with valuable informationregarding the dyskinesia induced by each particular dose throughout thecourse of the day. FIG. 15 also plots the value of BK scores throughoutthe day.

FIG. 16 plots DK and BK scores for another patient, who can be seen fromthese results to be very dyskinetic. This patient's BK scores arelargely normal thus providing a neurologist with valuable insight thatmedication might be lowered as bradykinesia has been fully treated buthigh dyskinesia is occurring. The aberrant BK scores around 8:15 AMmight have been caused by the patient removing the logger for examplewhen taking a shower.

On testing a form of the device and system on test subjects thefollowing occurred: Twelve subjects, patients with Parkinson's Disease,and eight healthy subjects (controls) were studied (Table 1). Subjectswere recognised as being bradykinetic [B], dyskinetic [D] and normal[C]. The Parkinson's Disease patients were drawn from one clinic andwere receiving medication for Parkinson's Disease. The controls had noknown neurological disorders. All procedures complied with the WorldMedical Association Declaration of Helsinki and were approved andsupervised by a Human Research & Ethics Committee. All subjects providedconsent following a detailed explanation of the experimental procedure.

TABLE 1 Subjects Normal Subjects (C) 8 (4 F) Average age, 48 ± 13Parkinson Subjects 11 (7 F) Average age, 67 ± 8 6 Bradykinetic (withouttremor, B) 6 Dyskinetic (D) 5 Disease duration 9 ± 4 Age at diseaseonset 58 ± 10Treatment of L-Dopa

To ensure that the patients were bradykinetic at time zero, they wererequested to withhold their regular therapy 10 hours prior tocommencement of the study. Food and fluid intake was not restricted. Asingle tablet of 250 mg of L-dopa and 25 mg of carbidopa was given tothe patients at the beginning of the study (0 minutes). The patientswere then requested to complete a set of simple tasks administered at 0,10, 20, 30, 45, 60, 90, 120, and 180 minutes after drug administration.

Clinical Assessment.

Bradykinesia was assessed by measuring maximum acceleration whileperforming a repetitive, oscillatory movement. Subjects were asked toslide their forefinger between two large dots (diameter 30 mm) placed300 mm apart on a piece of cardboard. This was performed for 30 secondsat their own pace, followed by a 30 second rest and then repeated asfast as possible for 30 seconds. The dots were positioned so that thelimb movement was across the body rather than to and from the body. Thiswas a variation on the well known and validated key press or peg boardtests for assessing bradykinesia. The averaged peak acceleration (APA)was the median of the 20 greatest accelerations and was used as theclinical bradykinesia score.

A dyskinesia score was obtained from the average of scores provided bytrained neurologists familiar with Parkinson's disease and experiencedin the use of the modified IMS scoring method. Two of the evaluators hadnot previously examined any of the patients used in this study; thethird evaluator provided their routine neurological care. The evaluatorsscored independently of their colleagues.

Subjects were videoed while they performed 5 specified tasks (describedlater). The video was divided into 30 s epochs and the evaluatorsprovided a score for each epoch. A Modified Involuntary Movement Score(IMS), modified from previously described methods was used to provide ascore of 0-4 for each of the following five body regions: Upperextremities; arms, wrists, hands and fingers, Lower extremities; legs,knees, ankles and toes, Trunk movements; back, shoulders and hips: Headmovements; neck and facial: Global Judgments; overall severity ofdyskinesias. The scores were as follows: 0=no dyskinesia present:1=dyskinesias discernable to a trained physician, except not alayperson: 2=dyskinesias easily detectable: 3=dyskinesias that wouldaffect day-to-day activities but do not restrict them: 4=dyskinesiasthat would restrict day-to-day activities. Thus the maximum IMS was 20.

Test Procedures

The accelerometer was oriented so that it was most sensitive topronation/supination movements and was attached to the most severelyaffected limb of Parkinsonian subjects and on the dominant limb ofcontrol subjects. The lead of the accelerometer was secured separatelybelow the elbow, so as to prevent adventitial movement of theaccelerometer. Subjects then performed the following tasks.

Task 1. Unrestricted Voluntary Movement:

Subjects were engaged in conversation about a subject that requireddescriptions of how to make, build or do something, such as tying a necktie. Spontaneous movements were recorded to establish whetherbradykinesia and dyskinesia could be detected using the spectrogram,during normal activities and not only during specially selected tasks.

Task 2. Voluntary Repetitive Alternating Movements:

This was described previously (clinical assessment) and was used toobtain a clinical bradykinesia score.

Task 3. Restricted Voluntary Movement:

Subjects were requested to remain as still as possible in an attempt toidentify involuntary movement, such as dyskinesia. The subjects wereinstructed to sit upright with their hands on their knees and wererequested to refrain from voluntary movement for 1 minute. Subjects werescored for dyskinesia during this task.

Task 4.

Patients poured water from a 1 L jug, filled to 600 ml, into threeplastic 250 ml cups. This task took between half a minute to two minutesto perform. Patients were asked to pour using the wrist with theaccelerometer attached.

Task 5.

The patients walked a distance of 2.5 metres turned 180° and walked afurther 2.5 metres. This was repeated for at least 30s although somesubjects took a minute to perform one cycle. One patient was confined toa wheelchair and was unable to perform this task.

Each task took approximately 2 minutes to perform. In the first part ofthe study, the subjects completed the first three tasks once. Followingthe test dose of L-dopa, subjects were requested to perform all fivetasks at regular intervals after drug administration. This trial wasdesigned to encompass the effects of a single dose of L-dopa and includethe consequent short-term motor fluctuations.

Statistical Analysis

The 0.5-8.0 Hz frequency band was divided into three bins or bands offrequency: 0.5-2.0 Hz, 2.0-4.0 Hz, and 4.0-8.0 Hz (FIG. 7 ). Thefrequency bands were selected to represent frequencies that may berelevant to specific movement behaviours. As the FFT is a line drawnthrough a series of discrete points, all the points in a band could besummed and averaged to produce a mean that will be subsequently referredas the MSP (mean spectral power) for the frequency band. ThusMSP^(0.5-2.0 Hz) will refer to the mean spectral power from thefrequency band of 0.5 to 2.0 Hz.

In the first stage of the study a comparison was made between the MSPobtained from the bradykinetic and the dyskinetic subjects using theMann-Whitney test and a P value less than 0.01 was consideredsignificant. Even though tests for statistical significance wereperformed, the only functionally useful result would be to achievelittle or no overlap between various clinical groups for a particulartest.

Results

Selection and Characterisation of Bradykinesa and Dyskinesia in Subjectswith Parkinson's Disease.

Patients in this study were selected because they had either obviousbradykinesia (known as bradykinetic patients) or prominent dyskinesiafollowing a dose of L-dopa (dyskinetic subjects). Bradykinetic subjectswere assessed when off medication but most did not develop prominentdyskinesia when on L-dopa. We used the APA (described in the methods)from the dot slide, as the ‘standard’ for bradykinesia severity. The APAscores of dyskinetic subjects was intermediate between normal andbradykinetic. A total IMS score was provided by three neurologists whogave a dyskinesia score for each two minute segment of videoed movement.Agreement between the three evaluators was reflected in the strongcorrelations between their scores

TABLE 2 Spearman Rank order correlations between the differentevaluators' scores of dyskinesia. Elevator 2 Elevator 3 Elevator 1 r =0.796 r = 0.860 Elevator 2 R = 0.915 All r values were significant (p <0.01)

Importantly, the IMS score for the recorded arm correlated highly(r=0.85, see also FIG. 13 ) with the total IMS score justifying themeasurement of acceleration in a just one arm (FIG. 13 ).

The next set of studies addressed the question of whether the Powerspectrum of normal subjects was suitable for identifying differentmovements. When a normal subject was sitting still (task 3, FIG. 6A),the power across the broad range of studied frequencies was lower thanwhen the subject was engaged in voluntary movement (task 1, FIG. 6A). Tothen demonstrate that 2 and 4 Hz limb oscillations could be measured,normal subjects used their fore finger to track 2 and 4 Hz oscillationson an oscilloscope screen. Clear peaks at the relevant frequencies wereapparent in the power spectrum (FIG. 6B). When subjects wrote the word“minimum”, a broad peak at approximately 3 Hz was apparent (FIG. 7 ).

The Power Spectrum was then divided into three bands (FIG. 7 ) and theMSP of each band was estimated (FIG. 8 ). When moving the wrist duringconversation (task 1), power spectrum in all three frequency bands waslower in bradykinetic subjects than in normal subjects (FIG. 8 ). Notsurprisingly this difference was less apparent when subjects were askedto remain still: bradykinetic patients could keep as still as normalsubjects (e.g. task 3, FIG. 8 ).

The frequency range of dyskinetic movements was similar to normalmovements but with a substantially increased power. As might beexpected, dyskinetic subjects had difficulty remaining completely still(task 3 FIG. 8 ). Although the MSP of normal and dyskinetic subjects wascompletely separated in each of the three spectral bands, the separationwas greatest in the MSP^(2.0-4.0 Hz) (task 3 FIG. 8 ).

Bradykinesia

The MSP^(2.0-4.0 Hz), from all patients at all time points werecorrelated with the APA measured at the same time point

TABLE 3 Pearson correlations (n = 79 for all Tasks) between theMSP^(2.0-4.0 Hz) and APA. Task 1 Task 3 Task 4 Task 5 Talking FreelySitting Still A Pouring Water Walking 0.320* r = 0.146 r = 0.400* r =0.264 *= significant r values (p < 0.01)

MSP^(2.0-4.0 Hz) correlated poorly with bradykinesia (as measured by theAPA). This was reflected in a low specificity (76%) and sensitivity(65.1%) of the MSP^(2.0-4.0 Hz) to predict bradykinesia.

The poor correlations most likely arise because bradykinesia measured byMSP was task dependent. For example, when a normal person “chose” to sitstill, the MSP would be indistinguishable from a bradykinetic, who doesnot have the capacity to move faster. Thus, the requirement was torecognise patients who were still for much of the time but capable ofmaking rapid movements from bradykinetic patients who were not capableof fast movements. On consideration, bradykinetic subjects make fewermovements than normal subjects and hence there are longer intervalsbetween movements. Furthermore, when bradykinetics movement occurs, themovements are of lower power, reflecting lower acceleration andamplitude.

An algorithm in accordance with one embodiment of the present inventionwas thus developed which, in essence, used the maximum acceleration madein each interval and the MSP in the period surrounding this peak toproduce an ABS (automated bradykinesia score). The argument was thatnormal subjects may have periods of low MSP but whatever movements theydo make would be done with much higher acceleration than bradykineticsubjects. The algorithm used to derive the ABS was modified serially andoptimised against the APA. When optimal, a new set of data was collectedand plotted against the APA (FIG. 9A). The ABS strongly correlated withthe bradykinesia “standard” (r=0.628, p<0.001, n=79) with a specificityof 87.5% sensitivity of 94.5%. The APA and the ABS were plotted againsttime after a dose of L-Dopa and the example of one subject is shown inFIG. 9B. In this case the correlation between APA and ABS was r=0.77.

Dyskinesia

An Automated Dyskinesia Score (ADS) was also developed. The ClinicalDyskinesia Score was found to be strongly correlated with both theMSP^(1-4 Hz) and the APA

TABLE 4 Pearson's correlations between the MSP^(2.0-4.0 Hz), the APA andthe Clinical Dyskinesia Score. APA Clinical Dyskinesia Score MSP r =0.90 r = 0.89 APA r = 0.85 All r values were significant (p < 0.01).

In view of these correlations, either accelerometer measure wouldprovide an objective measure of dyskinesia that would concur withneurological assessment. However, the sensitivity (76.9%) andspecificity (63.6%) of the MSP was unacceptably low. The correlation washighly dependent on the task being performed by the patient. Inparticular, this correlation did not take into account dyskinesia whenthe subject was sitting still, and the level of dyskinesia was markedlyhigher when the subject was walking even though it occurred only 30seconds later. Thus the problems with Spectral power as a measure ofdyskinesia were similar to those encountered with bradykinesia: namely,the problem of distinguishing between periods of increased voluntarymovement and increased involuntary movement (dyskinesia). Examination ofdyskinetic subjects and discussion with neurologists suggested thatdyskinetic subjects would have shorter time periods without movement.

Thus, in accordance with one embodiment of the invention, a DK algorithmwas developed to identify periods where movement was absent or of lowamplitude in the accelerometer recording. In brief, the meanacceleration in each 2 minute segment was estimated and movements aboveaverage acceleration were regarded as either voluntary or dyskineticmovements. Epochs where acceleration was less than the mean wereextracted and the MSP^(1.0-4.0 Hz) was divided by the number of lowacceleration epochs to provide an Automated Dyskinesia Score (ADS).Non-dyskinetic subjects should have greater periods below the meanacceleration and a lower MSP^(1.0-4.0 Hz), Dyskinetic subjects on theother hand should have less time below the mean acceleration window, andshould have a large MSP^(1.0-4.0 Hz). In essence the approach is toquantify the duration of time that the subject remains still. Thealgorithm of this embodiment used to derive the ADS was modifiedserially and optimised against the IMS. When optimal, a new set of datawas collected and plotted against the IMS and a correlation co-efficient(Spearman's) was calculated (r=0.766, p<0.0001, n=85, FIG. 10A). Whilethis correlation was less than that between MSP^(1-4 Hz) and the IMS,the sensitivity and specificity was much higher (sensitivity=84.6%,specificity=93.6%). The method was better suited for long-term recordingof patients, because it was less influenced by the type of taskperformed.

An assumption underlying these embodiments of the invention was thatpatterns of movement recognised by a trained observer can be quantifiedby recording a trace of the movement and modelling the features that theobserver uses to characterise the pattern. In this study we first showedthat spectral analyses could distinguish between bradykinesia anddyskinesia. However the sensitivity and selectivity of this methoddegraded when a variety of activities occurred.

In particular more complex analysis was required to distinguish betweenbradykinesia and a normal subject sitting still, and between dyskinesiaand some forms of normal activity. This was achieved by modelling whattrained observers see: bradykinetic subjects have longer intervalsbetween movement and when they do move it is with lower acceleration.Dyskinetic subjects have fewer intervals between movement and they movewith a greater spectral power. Using this approach it was possible torecognise bradykinetic and dyskinetic movements with high selectivityand sensitivity across a range of naturalistic movements.

To verify this embodiment of the invention involved reference to a “goldstandard”. Clinicians know dyskinesia and bradykinesia when they see itand clinical scales have been developed in an attempt to quantifyclinical observation. However these scales are subjective, requiretraining and experience and are most precise when repeated by the sameclinician. Of necessity these scales can only be used when a trainedobserver is present, but Parkinson's Disease varies greatly over theday, from day to day and one single snap shot cannot provide a truemeasure of function or fluctuation in disease. The bradykinesia anddyskinesia rating scales used are the most widely acceptedsemi-objective methods available to compare with the output of spectralanalyses. The most common clinical bedside test for bradykinesia is torequest rapid alternating finger movements. Slow small amplitudemovements (low acceleration) are considered bradykinetic and there areseveral quantitative scales that measure peak acceleration developedduring oscillatory movement such as peg board, key press and dot-slide(task 2). These vary according to the number of repetitions or, timingof movement or “amount” of movement achieved. Similarly, low amplitudeslow handwriting and key presses per minute are well-validated tests forbradykinesia. Each of these scales depends on the inability to reachnormal acceleration as a measure of bradykinesia. The dyskinesia scorewas a modification of other dyskinesia rating scales. The degree ofcorrelation between the clinical scales and the automated scales of thepresent embodiment suggest that the automated scales are of value andcould be used to continually score the clinical state over a protractedperiod. The DK and BK scores are capable of recognising the clinicalstates and may thus provide an effective clinical tool.

Thus, the described embodiment of the invention recognises that improvedmanagement of PD by medication requires monitoring of both bradykinesiaand dyskinesia, even when away from clinical observation, throughout theday. The present embodiment thus provides a means to remotely andsubstantially continuously capture, interpret and report a patient'smovement status over a defined period of time. Because this systemreports automatically to the neurologist, there is no need for thepatient or their carer to worry about remembering, keeping ormaintaining records. Further, the simple wrist-worn device of thisembodiment is easy to use and can be used at home or elsewhere and doesnot intrude on day to day activities, being a simple system that doesnot require an understanding of technology. Further, for people livingin rural and remote areas who are unable to easily attend clinics inmajor centres, changes to dosage can be made by the neurologistremotely, in conjunction with the patient's local GP.

The presently described embodiment of the invention is furtherbeneficial to the neurologist by automatically providing the neurologistwith an objective assessment (in digital report format) of the symptomsexperienced by patients with Parkinson's Disease (PD). This providesneurologists with reliable information about a patient's kinetic statusover a meaningful period, based on objective and continuous datacapture. With this information, physicians can titrate medication moreefficiently to reduce the incidence of dyskinesia and bradykinesia, keysymptoms for PD sufferers. This results in improved patient managementand a better quality of life for people living with PD. This may furtherresult in fewer visits to doctors/clinics, allowing a neurologist toprovide effective care to a greater number of patients.

Wider benefits of this embodiment may include improved patientmanagement that decreases the financial burden on health care systems,fewer day patient visits, reduced incidence of symptom-associated fallsand complications requiring hospitalization, and reduced high andspecialised aged care.

This embodiment further provides for the wrist-worn device to beprogrammable whereby the neurologist can set the time and frequency forrecording, based on the needs of the patient, and can further cause thedevice to give reminders to the patient for taking medication.

This embodiment thus provides an objective reporting tool that remotelyrecords PD patients' movements on a continuous basis and provides anassessment every 2-3 minutes, for the number of days required by theneurologist. It solves the problem of reliable measurement of PDsymptoms and automatically provides reports to the neurologist via emailor a suitable website. While helpful for all PD stages, it isparticularly valuable during the middle stages of the disease, whendyskinesia begins to emerge. Physicians can diagnose disease progressionand change medication dosage based on objective data recorded for 3-4days before a patient's visit. They can determine dosage effectivenessor make further changes using data recorded after dosage is altered.Records are easy to retain with the patient's history.

The present embodiment thus provides an objective continuous assessmentof the symptoms experienced by patients with Parkinson's disease. Thisembodiment may thus assist physicians to more inefficiently determineinstances of bradykinesia and dyskinesia and therefore improve patientmanagement by providing better medication, giving improved quality oflife for people with bradykinesia and/or dyskinesia, such as personshaving Parkinson's disease.

Some portions of this detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

As such, it will be understood that such acts and operations, which areat times referred to as being computer-executed, include themanipulation by the processing unit of the computer of electricalsignals representing data in a structured form. This manipulationtransforms the data or maintains it at locations in the memory system ofthe computer, which reconfigures or otherwise alters the operation ofthe computer in a manner well understood by those skilled in the art.The data structures where data is maintained are physical locations ofthe memory that have particular properties defined by the format of thedata. However, while the invention is described in the foregoingcontext, it is not meant to be limiting as those of skill in the artwill appreciate that various of the acts and operations described mayalso be implemented in hardware.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the description, it isappreciated that throughout the description, discussions utilizing termssuch as “processing” or “computing” or “calculating” or “determining” or“displaying” or the like, refer to the action and processes of acomputer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

The present invention also relates to apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but is not limited to, any type ofdisk including floppy disks, optical disks, CD-ROMs, andmagnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any typeof media suitable for storing electronic instructions, and each coupledto a computer system bus.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description. Inaddition, the present invention is not described with reference to anyparticular programming language. It will be appreciated that a varietyof programming languages may be used to implement the teachings of theinvention as described herein.

A machine-readable medium includes any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer). For example, a machine-readable medium includes read onlymemory (“ROM”); random access memory (“RAM”); magnetic disk storagemedia; optical storage media; flash memory devices; electrical, optical,acoustical or other form of propagated signals (e.g., carrier waves,infrared signals, digital signals, etc.); etc.

Turning to FIG. 12 , the invention is illustrated as being implementedin a suitable computing environment. Although not required, theinvention will be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a personal computer. Generally, program modules includeroutines, programs, objects, components, data structures, etc. thatperform particular tasks or implement particular abstract data types.Moreover, those skilled in the art will appreciate that the inventionmay be practiced with other computer system configurations, includinghand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, and the like. The invention may be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

In FIG. 12 a general purpose computing device is shown in the form of aconventional personal computer 20, including a processing unit 21, asystem memory 22, and a system bus 23 that couples various systemcomponents including the system memory to the processing unit 21. Thesystem bus 23 may be any of several types of bus structures including amemory bus or memory controller, a peripheral bus, and a local bus usingany of a variety of bus architectures. The system memory includes readonly memory (ROM) 24 and random access memory (RAM) 25. A basicinput/output system (BIOS) 26, containing the basic routines that helpto transfer information between elements within the personal computer20, such as during start-up, is stored in ROM 24. The personal computer20 further includes a hard disk drive 27 for reading from and writing toa hard disk 60, a magnetic disk drive 28 for reading from or writing toa removable magnetic disk 29, and an optical disk drive 30 for readingfrom or writing to a removable optical disk 31 such as a CD ROM or otheroptical media.

The hard disk drive 27, magnetic disk drive 28, and optical disk drive30 are connected to the system bus 23 by a hard disk drive interface 32,a magnetic disk drive interface 33, and an optical disk drive interface34, respectively. The drives and their associated computer-readablemedia provide nonvolatile storage of computer readable instructions,data structures, program modules and other data for the personalcomputer 20. Although the exemplary environment shown employs a harddisk 60, a removable magnetic disk 29, and a removable optical disk 31,it will be appreciated by those skilled in the art that other types ofcomputer readable media which can store data that is accessible by acomputer, such as magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memories, read only memories,storage area networks, and the like may also be used in the exemplaryoperating environment.

A number of program modules may be stored on the hard disk 60, magneticdisk 29, optical disk 31, ROM 24 or RAM 25, including an operatingsystem 35, one or more applications programs 36, other program modules37, and program data 38. A user may enter commands and information intothe personal computer 20 through input devices such as a keyboard 40 anda pointing device 42. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit21 through a serial port interface 46 that is coupled to the system bus,but may be connected by other interfaces, such as a parallel port, gameport or a universal serial bus (USB) or a network interface card. Amonitor 47 or other type of display device is also connected to thesystem bus 23 via an interface, such as a video adapter 48. In additionto the monitor, personal computers typically include other peripheraloutput devices, not shown, such as speakers and printers.

The personal computer 20 may operate in a networked environment usinglogical connections to one or more remote computers, such as a remotecomputer 49. The remote computer 49 may be another personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically includes many or all of the elements described aboverelative to the personal computer 20, although only a memory storagedevice 50 has been illustrated. The logical connections depicted includea local area network (LAN) 51 and a wide area network (WAN) 52. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets and, inter alia, the Internet.

When used in a LAN networking environment, the personal computer 20 isconnected to local network 51 through network interface or adapter 53.When used in a WAN networking environment, the personal computer 20typically includes modem 54 or other means for establishingcommunications over WAN 52. The modem 54, which may be internal orexternal, is connected to system bus 23 via the serial port interface46. In a networked environment, program modules depicted relative to thepersonal computer 20, or portions thereof, may be stored in the remotememory storage device. It will be appreciated that the networkconnections shown are exemplary and other means of establishing acommunications link between the computers may be used.

It will be appreciated by persons skilled in the art that numerousvariations and/or modifications may be made to the invention as shown inthe specific embodiments without departing from the scope of theinvention as broadly described. For example, while the describedembodiments relate to obtaining a dyskinesia score and a bradykinesiascore for an idiopathic Parkinson's disease patient treated with L-Dopa,it is to be appreciated that either score may be obtained alone, andeither or both scores may be obtained for a person experiencing kinesicsymptoms from other causes.

With regard to the Bradykinesia Scoring Algorithm, in BK7 the value ineach of the sub-bands A to H identified in BK6 were weighted andMSP_(MAX) was identified from the weighted band values. The BradykinesiaScore was then calculated according to the equation BK=10 log10(MSPmax×PKi) defined in BK10. In an optional embodiment, a single 0.8Hz sub-band which contains the maximum mean spectral power SP_(maxi) maybe identified and may replace MSPmax.

In DK4 of the Dyskinesia Scoring Algorithm Acc_(i) is used as athreshold below which data is deemed to represent “reduced movement”. Inthis, or an optional embodiment, a minimum threshold for Acc_(i) couldbe set at for example an arbitrary low level or generated in response toa very low BK score.

It is to be appreciated that the present invention could for example beapplied to individual assessment of hyperkinetic movements such asdystonia, chorea and/or myoclonus. The dyskinesia assessed byalternative embodiments of the present invention could for example arisefrom Huntington's disease, cervical dystonia, restless legs syndrome,paroxysmal kinesigenic dyskinesia, sleep disorders of movement, tics(stereotyped movements that are normal but out of context), Tourettessyndrome, tardive dyskinesia, tardive Tourettes, Halaroidan,Acanthocytosis, Hallervorden-Spatz or Pantothene Kinase deficiency, orSagawa syndrome.

The bradykinesia or hypokinetic movement assessed by alternativeembodiments of the present invention could arise from Multi SystemsAtrophy, Striatonigral degeneration, progressive Supranuclear palsy,Olivopontocerebellar degeneration, Corticobasal ganglionic degeneration,Huntington's disease, drug induced Parkinsonism, trauma inducedParkinsonism, Pallido Luysian degeneration or Vascular Parkinsonism.

Another embodiment of the invention comprises an accelerometer (ADXL330)which is sampled by a Philips ARM-Based Microcontroller LPC2138 and datais stored onboard the device in an SD-Flash Memory card for later manualdownload to PC for analysis. The device is programmed to record from thepatient for 16 hours per day for 4 days without recharge because thepatients have difficulty with conventional charging. Data is date andtime stamped and includes a header with patient details. The serviceprovider is able to program the patient details, time of start and endfor each day, and the number of days to record; all to be stored indatafile header.

The device records acceleration in three axes; X, Y, Z using a DC-10 Hzbandwidth (sampled @ 100 Hz per channel). The signal is calibrated in“gravity—g” and acceleration is measured from between +4 g and −4 g. AReal Time Clock is able to be programmed by the neurologist or serviceprovider to start recording at some prescribed date and time in thefuture. Most likely the next day and first thing in the morning. Thedevice records for a default time span of 6:00 am to 10:00 pm each day,but this time span is programmable by the neurologist or serviceprovider. The number of days of recording defaults to 3 fill days, butcan be programmed in the range of 1 to 7 days or more. This devicefurther provides for an input to be captured of a date and time thatmedication was taken. This could be the patient communicating with thewrist device to signal that medication has been taken.

At night, when the patient is in bed and the data logger is removed fromthe wrist, the datalogger will be placed in a cradle for batterycharging and downloading of data to the central server or the doctor'sown server.

The present embodiments are, therefore, to be considered in all respectsas illustrative and not restrictive.

We claim:
 1. An automated method of determining a bradykinetic state ofa person with Parkinson's disease, the method comprising: receiving atime series of motion data obtained from a motion detector worn on anextremity of the person over an extended period during non-clinicallyprompted activities of the person; applying, by a processor, a band passfilter to the motion data to extract filtered data for a band ofinterest, wherein the band of interest has a lower end cut off frequencyof 0.05 Hz and an upper end cut off frequency of 15 Hz; identifying, bythe processor, one or more movements of peak acceleration in thefiltered data by: extracting a plurality of bins of motion data from thefiltered data; and for each of the plurality of extracted bins offiltered data, identifying a maximum acceleration value, wherein forthat extracted bin of filtered data, the maximum acceleration valuerepresents a movement of peak acceleration of the one or more movementsof peak acceleration; and from the identified one or more movements ofpeak acceleration, determining, by the processor, a measure ofbradykinetic state; identifying bradykinetic motions by: (i)calculating, by the processor, spectral content of a sub-bin of motiondata centered on each of the movements of peak acceleration, and (ii)determining, by the processor, bradykinetic motions to exist when thereis a plurality of low frequency components of less than 4 Hz in thespectral content; and responsive to the processor determining themeasure of bradykinetic state, displaying, by a display device, avisible output representing an objective assessment of the bradykineticsymptoms of Parkinson's disease experienced by the person, wherein atleast one of an amount of medication in a dose and a timing of the doseof medication is caused to be altered responsive to the displayedvisible output being outside a range for a designated period of time. 2.The automated method of claim 1, further comprising repeating thereceiving, applying the band pass filter, and identifying one or moremovements of peak acceleration, to produce a time series of measures ofa kinetic state.
 3. The automated method of claim 2, further comprisinganalysing, by the processor, temporal characteristics of the time seriesof measures of the kinetic state.
 4. The automated method of claim 3,wherein analysing the temporal characteristics comprises determining, bythe processor, a moving mean of the time series of measures of thekinetic state.
 5. The automated method of claim 4, wherein the movingmean is applied over at least 10 minutes.
 6. The automated method ofclaim 3, wherein analysing the temporal characteristics comprisesdetermining, by the processor, a cumulative sum of individual measuresof the kinetic state.
 7. The automated method of claim 3, whereinanalysing the temporal characteristics comprises determining, by theprocessor, a percentage of time for which the person is in each of aplurality of kinetic state categories.
 8. The automated method of claim3, wherein analysing the temporal characteristics comprises: comparing,by the processor, data comprising the time series of measures of thekinetic state with a threshold value; and calculating, by the processor,a period of time for which the compared data remains below the thresholdvalue, wherein the calculated period of time for which the compared dataremains below the threshold value indicates a time of reduced movementof the person.
 9. The automated method of claim 8, wherein the thresholdvalue is a mean value of the data comprising the time series of measuresof the kinetic state.
 10. The automated method of claim 8, furthercomprising comparing, by the processor, the time of reduced movement ofthe person with a reference value and, when the time of reduced movementis less than the reference value, determining, by the processor, anincreased likelihood of dyskinesia in the person.
 11. The automatedmethod of claim 1, further comprising determining, by the processor, ameasure of kinetic state comprising a measure of one or more ofdyskinesia, hyperkinesia and tremor.
 12. The automated method of claim1, further comprising determining, by the processor, a measure ofkinetic state comprising a measure of quiet time.
 13. The automatedmethod of claim 2, wherein the time series of measures of the kineticstate is obtained during a medication period.
 14. The automated methodof claim 2, wherein the time series of measures of the kinetic state isobtained throughout a day.
 15. The automated method of claim 3, whereinanalysing the temporal characteristics comprises automaticallydetermining, by the processor, existence of a shift in a centraltendency of the time series of measures of the kinetic state.
 16. Theautomated method of claim 15, wherein the central tendency is selectedfrom a group comprising mean, median, mode or variance of datacomprising the time series of measures of the kinetic state.
 17. Theautomated method of claim 1, wherein the time series of motion data isobtained while the motion detector is oriented to be sensitive to atleast one of pronation and supination movements of the person.
 18. Anon-transitory computer readable-medium comprising instructions storedthereon, that when executed by a processor, determine a bradykineticstate of a person by: receiving a time series of motion data from amotion detector worn on an extremity of the person over an extendedperiod during non-clinically prompted activities of the person; bandpass filtering the motion data to extract filtered data for a band ofinterest, wherein the band of interest has a lower end cut off frequencyof 0.05 Hz and an upper end cut off frequency of 15 Hz; identifying oneor more movements of peak acceleration in the filtered data by:extracting a plurality of bins of motion data from the filtered data;and for each of the plurality of extracted bins of filtered data,identifying a maximum acceleration value, wherein for that extracted binof filtered data, the maximum acceleration value represents a movementof peak acceleration of the one or more movements of peak acceleration;and determining from the identified one or more movements of peakacceleration a measure of bradykinetic state; identifying bradykineticmotions by: (i) calculating spectral content of a sub-bin of motion datacentered on each of the movements of peak acceleration, and (ii)determining bradykinetic motions to exist when there is a plurality oflow frequency components of less than 4 Hz in the spectral content; andresponsive to the processor determining the measure of bradykineticstate, displaying, by a display device, a visible output representing anobjective assessment of the bradykinetic symptoms of Parkinson's diseaseexperienced by the person, wherein at least one of an amount ofmedication in a dose and a timing of the dose of medication is caused tobe altered responsive to the displayed visible output being outside arange for a designated period of time.
 19. The non-transitory computerreadable-medium of claim 18, wherein when executed by the processor, theinstructions cause the processor to repeat the receiving, band passfiltering, and identifying one or more movements of peak acceleration,to produce a time series of measures of a kinetic state.
 20. Thenon-transitory computer readable-medium of claim 19, wherein whenexecuted by the processor, the instructions cause the processor toanalyze temporal characteristics of the time series of measures of thekinetic state by one or more of: determining a moving mean of the timeseries of measures of the kinetic state; determining a cumulative sum ofindividual measures of the kinetic state; determining a percentage oftime for which the person is in each of a plurality of kinetic statecategories; and comparing data comprising the time series of measures ofthe kinetic state with a threshold value and calculating a period oftime for which the compared data remains below the threshold value,wherein the calculated period of time for which the compared dataremains below the threshold value indicates a time of reduced movementof the person.
 21. The non-transitory computer readable-medium of claim20, wherein when executed by the processor, the instructions cause theprocessor to compare the time of reduced movement of the person with areference value and, when the time of reduced movement is less than thereference value, determining an increased likelihood of dyskinesia inthe person.
 22. The non-transitory computer readable-medium of claim 18,wherein the time series of motion data is obtained while the motiondetector is oriented to be sensitive to at least one of pronation andsupination movements of the person.
 23. The automated method of claim 1,further comprising: calculating, by the processor, mean spectral powerfor data corresponding to the identified movements of peak acceleration,and determining, by the processor, the measure of bradykinetic statefrom the calculated mean spectral power.
 24. The automated method ofclaim 8, further comprising: calculating, by the processor and for thedata below the threshold value, mean spectral power; and determining, bythe processor, an increased likelihood of dyskinesia in the person whenthe calculated mean spectral power is higher than a reference value. 25.The automated method of claim 12, wherein quiet time is determined bythe processor calculating a threshold corresponding to a mode of thebradykinetic state measures determined for a plurality of bins of motiondata, and further calculating quiet time as a duration for whichbradykinetic state measures are below the calculated threshold.
 26. Theautomated method of claim 1, further comprising using, by the processor,the measure of bradykinetic state when the displayed visual output isoutside the range for the designated period of time to assesseffectiveness of therapy in treating the bradykinetic symptoms ofParkinson's disease experienced by the person.
 27. The automated methodof claim 1, wherein the displayed visible output being outside the rangefor the designated period of time is representative of the objectiveassessment of the bradykinetic symptoms of Parkinson's diseaseexperienced by the person correlating to a medication regime ofParkinson's disease being ineffective.
 28. The automated method of claim27, wherein the amount of medication in the dose is altered from a firstnon-zero dosage of medication to a second, different non-zero dosage ofmedication.
 29. The non-transitory computer readable-medium of claim 18,wherein the displayed visible output being outside the range for thedesignated period of time is representative of the objective assessmentof the bradykinetic symptoms of Parkinson's disease experienced by theperson correlating to a medication regime of Parkinson's disease beingineffective.
 30. The non-transitory computer readable-medium of claim29, wherein the amount of medication in the dose is altered from a firstnon-zero dosage of medication to a second, different non-zero dosage ofmedication.